In this paper, we validated the proposed two Blockformer methods (Base-WSBO and SE-WSBO) on two Mandarin speech recognition datasets: public AISHELL-1 corpus [1]}, internal 1400 hours corpus.
The AISHELL-1 corpus consists of 178 hours of labeled speech collected from 400 speaker with high fidelity microphone. We construct the decoding graph TLG using its annotated text as a corpus for language model. The 1400-hour internal corpus is collected from some service domains, such as medicine, cosmetic and estate, which is more diverse in data and more challenging in speech recognition. For the AISHELL-1 task, we use the 150-hour for training and the 18-hour development set for early-stopping.
The character error rate (CER%) is reported in the 7176-sentence test set (about 10 hours).
For the 1400-hour internal corpus, we use a 1400-hour for training, a 6-hour development set for early-stopping, and a 13-hour test set for evaluation.
